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Modeling the Evolution of Drug Resistance in Malaria

Maggie Eppstein, professor and chair of computer science and founding director of the Vermont Complex Systems Center, has long been interested in studying evolution on fitness landscapes in both evolutionary computation (search algorithms inspired by evolution) and computational evolution (using computational simulations to try to understand biological evolution). Over the past two years she has developed “a fun and productive collaboration” with evolutionary biologist Brandon Ogbunugafor, formerly a Henderson Fellow and now assistant professor in the UVM department of biology, modeling the evolution of drug resistance in malaria. In July, 2016, they won a ‘Best Paper’ award at the Genetic and Evolutionary Computation Conference for their paper entitled “Quantifying Deception: A Case Study in the Evolution of Antimicrobial Resistance”. Their latest paper “Competition Along Trajectories Governs Adaptation Rates Towards Antimicrobial Resistance” was published online on November 21, 2016, in the inaugural issue of Nature Ecology and Evolution. In this work, they show how one can predict how long it will take malaria to evolve resistance to different antimicrobial treatments, a finding that provides new insights into evolutionary processes and reveals potential pitfalls to be avoided in the development of new antimicrobial treatments. In an accompanying ‘Behind the Paper’ blog, Eppstein describes how electrical circuits sparked this breakthrough in evolutionary theory.